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Related Concept Videos

Pathophysiology of Heart Failure01:17

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Heart failure (HF) is a progressive syndrome involving ventricles that leads to inadequate cardiac output. It can be classified based on location and output or ejection fraction. Ejection fraction (EF) is an essential measurement in the diagnosis and surveillance of HF. Reduced EF corresponds to systolic heart failure (HFrEF). However, HF with preserved ejection fraction (HFpEF) is becoming increasingly prevalent. Also known as diastolic HF, this form of HF is related to aging. The...
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Depressive Disorders: Etiology01:27

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Depressive disorders result from a complex interplay of biological, psychological, and sociocultural factors, each contributing uniquely to the development and persistence of the condition. Understanding these factors provides critical insight into the multifaceted nature of depression.
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Heart Failure Drugs: Inhibitors of Renin-Angiotensin System01:26

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The activation of the sympathetic nervous system and the renin-angiotensin-aldosterone system (RAAS) contributes to cardiac remodeling, and inhibiting the RAAS is a pharmacological target in heart failure management. As a result, neurohumoral modulation is a crucial treatment principle for managing heart failure. This approach involves using medications like ACE inhibitors (ACEIs), angiotensin receptor blockers (ARBs), β-blockers, mineralocorticoid receptor antagonists (MRAs), and neutral...
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The heart's primary function is to pump blood throughout the body, maintaining a balance between blood sent out (cardiac output) and blood returning (venous return). If this balance is disrupted, it can result in congestive heart failure (CHF), a severe condition where the heart becomes an inefficient pump, leading to inadequate blood circulation.
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Heart failure and kidney perfusion are interconnected in a complex way. Reduced renal perfusion and venous congestion are two significant factors that contribute to renal dysfunction in heart failure. The kidneys, primarily responsible for fluid balance in the body, are adversely affected due to compromised cardiac output and increased venous pressure. In response to reduced renal perfusion, the kidneys activate neurohumoral mechanisms to restore balance. However, these mechanisms can be...
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Heart Failure Drugs: β-Blockers01:22

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β-adrenergic antagonists, commonly known as β-blockers, block the effects of sympathetic neurotransmitters such as noradrenaline (NA) and adrenaline (ADR). They have several beneficial effects in heart failure treatment. They reduce heart rate, the force of contraction, and cardiac muscle relaxation. They also slow the atrial-ventricular conduction rate and raise the threshold for arrhythmias. The concentration of β-blockers determines their effects on bronchodilation,...
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Predicting depression in patients with heart failure based on a stacking model.

Hui Jiang1, Rui Hu1, Yu-Jie Wang2

  • 1Department of Ultrasound, The Second Affiliated Hospital of Anhui Medical University, Hefei 230601, Anhui Province, China.

World Journal of Clinical Cases
|July 29, 2024
PubMed
Summary

A new stacking model effectively predicts depression in heart failure (HF) patients. This tool aids clinicians in early identification and intervention for at-risk individuals, improving mental health outcomes.

Keywords:
DepressionHeart failureMachine learningNational health and nutrition examination surveyStacking ensemble model

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Area of Science:

  • Cardiology
  • Psychiatry
  • Machine Learning

Background:

  • Limited research exists on using stacking ensemble algorithms for depression prediction in heart failure (HF) patients.
  • Depression is a significant concern in HF populations, impacting patient outcomes.

Purpose of the Study:

  • To develop and validate a stacking ensemble model for predicting depression in patients with heart failure.

Main Methods:

  • Utilized data from 1084 HF patients (National Health and Nutrition Examination Survey, 2005-2018).
  • Identified depression predictors using univariate analysis and artificial neural networks.
  • Constructed a stacking model with tree-based learners and interpreted it using SHapley additive exPlanations (SHAP).

Main Results:

  • The stacking model achieved an area under the curve of 0.77 (95% CI: 0.71-0.84), with 0.71 sensitivity and 0.68 specificity.
  • Model reliability was confirmed by calibration curves and clinical utility by decision curve analysis.
  • Age was identified as the most influential predictor in the stacking model via SHAP analysis.

Conclusions:

  • The developed stacking model shows robust predictive capability for depression in HF patients.
  • This model offers a valuable tool for clinicians to identify high-risk individuals, facilitating timely psychological interventions.